Multivariable Control

The course covers several aspects of model-based control and estimation methods. The focus is on industrial applications, implementation, real life problems, and hands-on experience. The course gives an overview of state-of-the-art techniques, and provides students with tools to analyse and solve further industrial and research problems. Strong emphasis is given to the use of numerical simulation and scientific programming with Matlab/Simulink or similar.

Basic course in control theory, linear systems and system dynamics. Basic understanding of matrix algebra, statistics, and programming using MATLAB or similar.

No formal requirements over and above the admission requirements.

Upon successful completion of the course, the student:

Knowledge:

• can develop data-driven dynamic modeling methods, can evaluate suitability of different model structures and choose appropriate models for chosen industrial application
• can develop and apply state estimation techniques for linear systems
• can develop control strategy and evaluate different model-based control algorithms for multivariate systems
• can select and argue for suitable combination of model-based control algorithms for a chosen industrial application.

Skills:

• can obtain dynamic models with data-driven dynamic modelling methods for a chosen industrial application
• can implement, test and validate linear techniques for state estimation
• can implement, test and validate model-based control algorithms for multivariate systems
• can implement, test and validate different model-based control and estimation algorithms in a simulation environment.

General competence:

• can develop, implement, test and validate control strategies for multivariate system using different model-based control and estimation methods.

• Data driven dynamic modelling (system identification)
• State estimation
• Model-based PID-tuning methods
• Multivariable control algorithms
• Predictive control algorithms
• Implementation, testing and validation with numerical simulation.

Weekly lectures and exercises, assignments in groups of 2-3 students and one individual semester project.

Two guest lectures on selected topics given by experts from industry and academia.

The following required coursework must be approved before the student can take the exam:

Four assignments in groups of 1 - 3 students (1000 - 2000 words per assignment)

The assessment will be based on two part-exams:

1) Individual project report (4000-6000 words). The project report counts 80% of the final grade.

2) Individual project presentation (10 minutes). The oral examination counts 20% of the final grade

Both exams must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All, computer with MATLAB and Simulink.

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Two internal examiners. External examiner is used periodically.

Associate Professor Tiina Komulainen